• AMIDST: A Java toolbox for scalable probabilistic machine learning 

      Masegosa, Andres; Martinez, Ana M.; Ramos-López, Dario; Cabañas, Rafael; Langseth, Helge; Nielsen, Thomas D.; Madsen, Anders L. (Journal article; Peer reviewed, 2018)
      The AMIDST Toolbox is an open source Java software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modelling language based on probabilistic ...
    • Analyzing concept drift: A case study in the financial sector 

      Masegosa, Andres; Martinez, Ana M.; Ramos-López, Dario; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio (Journal article; Peer reviewed, 2020)
      In this paper, we present a method for exploratory data analysis of streaming data based on probabilistic graphical models (latent variable models). This method is illustrated by concept drift tracking, using financial ...
    • MAP inference in dynamic hybrid Bayesian networks 

      Ramos-López, Dario; Masegosa, Andres; Martinez, Ana M.; Salmeron, Antonio; Nielsen, Thomas D.; Langseth, Helge; Madsen, Anders L. (Journal article; Peer reviewed, 2017)
      In this paper, we study the maximum a posteriori (MAP) problem in dynamic hybrid Bayesian networks. We are interested in finding the sequence of values of a class variable that maximizes the posterior probability given ...
    • Modeling concept drift: A probabilistic graphical model based approach 

      Borchani, Hanen; Martinez, Ana M.; Masegosa, Andres; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio; Fernandez, Antonio; Madsen, Anders L.; Sáez, Ramón (Journal article; Peer reviewed, 2015-11-22)
      An often used approach for detecting and adapting to concept drift when doing classification is to treat the data as i.i.d. and use changes in classification accuracy as an indication of concept drift. In this paper, ...
    • Probabilistic Models with Deep Neural Networks 

      Masegosa, Andres; Cabañas, Rafael; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio (Peer reviewed; Journal article, 2021)
      Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate ...
    • Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks 

      Ramos-López, Dario; Masegosa, Andres; Salmeron, Antonio; Rumi, Rafael; Langseth, Helge; Nielsen, Thomas D.; Madsen, Anders L. (Journal article; Peer reviewed, 2018)
      In this paper we propose a scalable importance sampling algorithm for computing Gaussian mixture posteriors in conditional linear Gaussian Bayesian networks. Our contribution is based on using a stochastic gradient ascent ...
    • Scaling up Bayesian variational inference using distributed computing clusters 

      Masegosa, Andres; Martinez, Ana M.; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio; Ramos-López, Dario (Journal article; Peer reviewed, 2017)
      In this paper we present an approach for scaling up Bayesian learning using variational methods by exploiting distributed computing clusters managed by modern big data processing tools like Apache Spark or Apache Flink, ...
    • Variational Inference over Nonstationary Data Streams for Exponential Family Models 

      Masegosa, Andres; Ramos-López, Dario; Salmeron, Antonio; Langseth, Helge; Nielsen, Thomas D. (Peer reviewed; Journal article, 2020)
      In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires ...